12/25/16

We
did not report on the difference between the core consumer price index (cCPI)
and the index for food (less beverages) since 2013,
when we reported the overall fall in food price in the USA. This update is a
bit late considering our promise to update at an annual rate, but the current
trend in the discussed difference is so strong and indicative that we could not
miss the opportunity to praise the success of our 8-year-old prediction.

So we
continue reporting on and predicting the evolution of the difference between the
core consumer price index (cCPI) and the index for food (less beverages). Previously, we confirmed in many posts (see
this blog) and papers [1, 2] that this difference had been
following a long-term negative and linear (time) trend since 2001. Originally, we predicted a
turn to a positive trend in 2014. Five years ago, we expected the turn
to a positive trend in 2012. In Figure 1, one can observe that the turn
actually occurred in November 2014 and the current trend is positive – the price
index of food grows at a lower rate than that of the core CPI.

For
an investor dealing with commodities, the index of food, which is growing at a
rate lower than the core CPI, is an important reference for any action. Food
price affects not only economic but also social and political processes.

Figure
1 depict the most recent period. In 2008, when we first addressed the issue of
sustainable trends in CPIs, the trend line was much steeper than now and intersected
the zero line in 2014. This was our
initial estimate of the turning point for the negative trend. The zero line was
considered as a natural level of reflection.
In the beginning of 2009, the difference reached the bottom and turned
to a positive one, although not for long. The growth in food prices restarted
in 2010. In the end of 2011, the difference had a short stop which we likely
misinterpreted as a manifestation of the transition to a positive trend. Since
October 2011, the difference has not been changing much with just a slight
positive trend. In 2014, the studied difference began to grow and will likely
grow another decade.

Food is getting cheaper in relative terms.

Figure 1. The
difference between the core CPI and the price index of food since 2002.

In
2008, we published a paper on the presence of
long-term sustainable trends in the differences between various components
of the CPI in the USA. We started with the difference between the core CPI
(i.e. CPI less food and energy) and the overall CPI. Two Figures below are
borrowed from the paper:

Figure 1. Linear regression of the
difference between the core CPI and CPI for the period from 1981 to 1999. The goodness-of-fit is 0.96, and the slope is
0.67.

Figure 2. Linear regression of the
difference between the core CPI and CPI after 2002. The goodness-of-fit is 0.86, and the tangent
is -1.57. An elevated volatility has
been observed from 2005.

In
this eight-year-old paper as well as in later papers on the sustainable trends
in the CPI and PPI (see here),
we suggested that the negative trend shown in Figure 2 should reach some bottom
point and then turn to a positive trend. It was
also mentioned that such processes in the past had been accompanied by an
elevated volatility in the difference, i.e. high amplitude fluctuations.

Eight
years later, we are happy to state that our predictions were accurate - the
trend now positive and is likely approaching the
mid-point of the linear segment. Since the very beginning, we have been
reporting on the evolution of the difference between the headline CPI and core
CPI many
times.

Our
concept of cyclic
evolution was formulated in 2007. Essentially, this concept says that the
future trajectory has to repeat the path observed thirty years ago, i.e. the
cycle has a 30 years period. Figure 3 presents the state of the CPI difference normalized
to the headline CPI as observed since the late 1950s (dotted red line) and the
predicted trajectory (blue dotted line), which is the current curve shifted by
30 years ahead. Figure 4 presents corresponding linear trends with all volatile
periods removed.

We
can say that eight years ago we successfully predicted the era of low
energy+food prices (core CPI is the headline CPI less food and energy) since
2014 and for the following 20 years. For energy companies, Figure 4 implies
that oil/energy prices will be on a negative trend until 2030. We are just in
the middle of the fall, which is supposed to be dramatic in the next two to
four years. All measured taken by oil-producing countries are worthless -

actual economic forces are beyond simple control.

Figure 3. The difference between
core (cCPI) and headline (CPI) CPI normalized to CPI. The period of cycle is 30
years and dotted blue line presents the
future of the normalized difference before 2044.

Figure 4. The current state of our
prediction. Red line follows up the blue line.

12/22/16

The International monitoring system of the CTBTO includes seismic station PPT, which is situated near Papeete, Tahiti. This station is a rather isolated one. Working with its data at an everyday basis I could not imagine how beautiful is the Earth for a space traveler flying directly to PPT. Google Earth gave me a striking view on the Papeete hemisphere.

The latter events in the USA and other countries demonstrate that interaction between inhomogeneous and dynamic society and frozen and irresponsible establishment is not ideological or sacral. It is rather economic. No pure idea or even patriotic war can defend real economic loss for a bigger part of population. People do perceive growing economic disparity, which is aslo seen in dry digits of economic reports. Here, I present one figure with two curves showing the degree of dramatic loss in the portion of personal income received by working people in the lower income range. First curve shows the portion of population (age 16 and above) with income as reported by the US Census Bureau and measured in the (March) Current Population Surveys (CPS). One can observe a longer period of healthy growth from the 1950s to the 1980s, mostly due to increasing female involvement. The portion of economically active population reached approximately 92% in 1980 and then did not change over 20 years. In 2001, a gradual decline started and that trend has been observed ever since. Therefore, the portion of people without any reportable personal income has been increasing and these people cannot fall below the zero income line. They have nothing to lose. This fall in the portion of people without personal income is not directly related to actions of any specific authority. It is a result of secular oscillation or Kondratiev wave, as described in one of my previous posts.

Second curve is more illustrative. The portion of CPS income in the GDP (or Gross Domestic Income – GDI = GDP) reached 68.4% in 1980. More important is that it does not show strong dependence on the growing portion of people with income before 1980. Data before 1967 are absent and we cannot make a better quantitative comparison between the portion of people with income and their share in the GDP for the whole period between 1950 and 1980. Between 1980 and 2000, the portion of income was on a gradual decline down to 64.5%, or by 4 % per 20 years. Since 2000, lets call is the Bush era, the portion of CPS income, which is mostly wages and salaries of employed people, has been falling very fast and reached 55% (!) in 2009. In 2015, the portion of population with income was 55%, i.e. it returned to the worst year of the economic depression after a short recovery.

Considering the overall fall in personal income for a bigger portion of population with smallest incomes one can mark a link between the epic failure of Clinton in the past presidential elections and the overall perception of economic disparity which contradicts the overall rhetoric of the American establishment. Then somebody speaks in the media about potential economic loss poor people are happy with such a perspective because they have less to lose and the level of economic disparity may decrease when the rich and richest really lose their money. From the point of view of economic inequality, the Clinton’s failure might be perceived as deserved for the supportive establishment, and thus, positive. The current establishment has failed to understand real social and economic processes and has to be removed together with its logistic support like think tanks, economic and social departments, and the mass media. They demonstrate linear thinking (tomorrow as today) and broad absence in understanding of actual processes.

I have compared the list of states for Clinton and the list of riches states in terms of average income per capita. They coincide by more than 80%. This does not prove the link between falling personal income and the degradation of the current establishment. But it is indicative.

Brexit likely had the same economic root. Other developed countries have to be aware of potential problems related to the side effect of Kondratiev wave and the growing income inequality. We are close to the middle of negative period in economic activity and the hardest time is likely five to ten years ahead of us.

The method of waveform cross correlation (WCC) allows remote monitoring of weak seismic activity induced by underground tests. This type of monitoring is considered as a principal task of on-site inspection under the Comprehensive nuclear-test-ban treaty. On September 11, 2016, a seismic event with body wave magnitude 2.1 was found in automatic processing near the epicenter of the underground explosion conducted by the DPRK on September 9, 2016. This event occurred approximately two days after the test. Using the WCC method, two array stations of the International Monitoring System (IMS), USRK and KSRS, detected Pn-wave arrivals, which were associated with a unique event. Standard automatic processing at the International Data Centre (IDC) did not create an event hypothesis, but in the following interactive processing based on WCC detections, an IDC analyst was able to create a two-station event . Location and other characteristics of this small seismic source indicate that it is likely an aftershock of the preceding explosion. Building on the success of automatic detection and phase association, we carried out an extended analysis, which included later phases and closest non-IMS stations. The final cross correlation solution uses four stations, including MDJ (China) and SEHB (Republic of Korea), with the epicenter approximately 2 km to north-west from the epicenter of the Sept. 9 test. We also located the aftershock epicenter by standard IDC program LocSAT using the arrival times obtained by cross correlation. The distance between the DPRK and LocSAT aftershock epicenters is 25.5 km, i.e. by an order of magnitude larger than that obtained by the WCC relative location method.

11/30/16

Currently, we observe the ultimate effect of western democracy, when politicians are allowed to compete for political power on a free market with any set of PR tools used against voters. There is no problem, which is not touched by politician if it can give extra votes. That makes each and every voter to decide which side s/he is thousands of times. This process effectively destructs the raw flesh of society - every person is disjointed from all others by attitude to one or many problems. The society looses the universal positive perspectives of future - it is atomized to the level when only negative reaction matters, like protests or flesh mobs. Democracy is not about common values any more - its is about my own values.

I guess that western democracy has come to dead end. There is no possibility to split people in smaller pieces. And any concept joining people will lose on the free electoral market before the society destruction will come to the level historical examples like in Italy, Spain, and Germany. In western countries, people are so disordered and confused that many of them trust even Russian propaganda.

5/29/16

Four years ago, we wrote in this blog about the strict proportionality between the CPI
inflation and the actual interest rate defined by the Board of Governors of the Federal Reserve System,
R. Briefly, the cumulative
interest rate is just the cumulative CPI times 1.4. There are periods when the
interest rate deviates from the long term inflation trend, which has been
almost linear since 1972. Here, we extend observational dataset and discuss the
most probable reason why the FRS actually not controlling inflation by
presenting the actual economic force behind price inflation, as we presented in
a series of papers [e.g., 1,
2,
3,
and 4]. Overall,
inflation is a linear lagged function of the change in labor force. The latter
is driven by a secular change in the participation rate in labor force (LFPR) together
with general increase in working age population. In other words, increasing
labor force inflate process and decreasing labor force leads to deflation.

Introducing new data obtained from 2012, we depict in
Figure 1the effective rate R divided by a factor of 1.37 (see our previous post
for details) and the consumer price inflation. One can see that R lags behind
the CPI since 1980, i.e. inflation grows at its own rate and R has to follow
up. The idea of interest rate is that a higher R should suppress price inflation
when it is high due to the effect expensive money. During deflationary periods with
slow economy, low (in some countries negative) R has to channel cheap money
into the economic growth. The reaction of inflation is also expected not shortly
but with some time lag. The

The cumulative influence of the interest rate should
produce a desired effect in the long run and inflation should go in the
direction towards acceptable values. Figure 2 displays the cumulative effect,
i.e. the cumulative values of the monthly estimates of R and CPI multiplied by
1.37. This is an intriguing plot. In the long run, the R curve fluctuates
around the CPI one and returns to it. It is hard to believe that the sign of
deviation of R from the 1.37CPI curve affects the behavior of the CPI, which is
practically linear. Therefore, the influence of monetary policy is under doubt.

The FRS has tried all means to return the CPI to R
without any success and have to return R to the CPI!

We have already described the secular changes in LFPR
in 2013,
2014,
and 2015.
Figure 3 illustrates the evolution of LFPR as measured by the Bureau of Labor
Statistics. The LFPR curve is accurately approximated by a simple function:
LFPR(t) = 62.7+4.3SIN(2π[t-1978]/T). The period T=74 years and the double
amplitude is 8.6, i.e. the largest LFPR change is 8.6%. Currently, the LFPR is
strictly in the center of the range and in the middle of the fall from 1996 to
2034.

Our concept is based on the observation that the
periods of high inflation are related to accelerated labor force growth. Therefore,
we have highlighted the most recent and the next period of accelerated growth
as marked red (start) and green (end) vertical lines highlight two periods. These
periods of accelerated growth lasts 1/4T =18 years. Figure 4 presents the first
and second time LFPR derivatives, which are used to select the accelerated
growth, i.e. the period when both derivatives are positive. There is a clear
coincidence between the period of two-digit inflation and the peak in the first
derivative near 1978. This is one of
many facts supporting our concept of inflation. This is not the purpose of this
post, however. Here, we compare the FRS decisions on discount rates and the
behavior of the LFPR curve.

Figure 5 compares the difference between the R and
1.37CPI in Figure 2 (red curve) and the product of the LFPR’ and LFPR’’, i.e.
the curve representing the change in acceleration. The latter curve is shifted
by 6 years back in time (phase shift of approximately -30 degrees for period of
74 years). The peaks in the difference curve are well synchronized with the
acceleration curve, which is leading by 6 years. In reality, FRS decisions are fully driven by
the LFPR. Moreover, the FRS is very slow in understanding status quo.

Now, R and 1.37CPI in Figure 2 coincide. This means that the best R has to be 1.37 of
the current CPI, but we all know that R will be retained below this value at
least before 2020. We are thinking now
on the investment opportunities resulting from the predictable FRS behavior.

Figure 1. The
federal funds rate, R, divided by 1.37 and the rate of consumer price
inflation, CPI, between 1955 and 2016.

Figure 2.
Cumulative values of the curves in Figure 1.

Figure 3. The rate of participation in labor force
(LFPR). LFPR is accurately approximated by a simple function: LFPR(t) =
62.7+4.3SIN([t-1978]/T). The period T=74 years. Red (start) and green (end)
vertical lines highlight two periods of accelerated growth. The periods of
accelerated growth lasts 1/4T =18 years. The next period will start in 2034.

Figure 4. First and second time derivatives of the approximating
SIN function.

Figure 5. The difference between the cumulative sum of
effective federal funds rate (monthly, not seasonally adjusted) and the
cumulative sum of the monthly rate (y/y) of consumer price inflation compared
to the acceleration periods in the LFPR.

The figure below is self-explanatory.
This is the cumulative real GDP growth in the former socialist countries (FSC) after
1990 (i.e. the past 25 years) as presented in the Total Economy Database. The most successful (>60%) countries are
Armenia, Azerbaijan, Belarus, Estonia, Kazakhstan, Poland, Slovakia,
Turkmenistan, and Uzbekistan. Three to four of them are recognized democracies
and the other five are under strong leadership (euphemism for pure economic
discussion). The absolute losers are
Tajikistan and Ukraine (the winner with -26.2%), Serbia and Montenegro, with
Moldova being still below the 1990 GDP level. All four countries have quite a
controversial political configuration. Other FSC are above the zero line ranging
from Croatia (4%), Kirgizia (7%) and Georgia (10%) to Latvia (55%), Bulgaria
(44%) and Slovenia (43%).

It is hard to deny the
general observation that strong leadership was able to create better economic
conditions for growth in the countries of the former Soviet Union, except
Baltic countries. Political turmoil is not creative, but we know it very well.

I would not invest in a country
without a stable political configuration.

Figure 1. The cumulative real GDP growth between 1990 and
2015 in the former socialist countries

5/23/16

Many European countries are missing in the
first part of this post. All they deserve to be presented but we illustrate the
diversity of and similarities in population trajectories rather than create a comprehensive view on
the development in EU demography. We still use the OECD database which allows covering the century between 1950
and 2050. Here we present a few older EU representatives together with newcomers.
Figure 1 demonstrates that three East
European countries: Poland, Bulgaria and Czech Republic and five western
countries with longer capitalist economic history. Germany serves as a watershed for these two
groups of countries.

Bulgaria shows behavior similar to that in Latvia and
Lithuania - extremely steep depopulation trajectory after 1990. According to
the OECD projection, Bulgaria will lose
from more than 40% of its population measured
in 1990. Depopulation is striking and
dangerous for survival as a nation. Poland and Czech Republic are similar to
Germany – approximately 5% to 10% fall in total population before 2050.

Switzerland looks to have all chances to
succeed in healthy population growth together with Austria, who also shows
gradual growth into the future. Spain, Italy and Holland are a bit
controversial but also have hopes for future population rise in the next
decades.

Taking into account France and the UK in the
previous post one can conclude that East European countries that entered the EU
are all are prone to depopulation of varying degree, while the founding members feel much better.

Figure 1.
the evolution of total population in selected EU countries between 1950 and
2050. All curves are normalized to their respective values in 1990.

Everybody knows that European Union is not
homogeneous. The idea behind unification was to overcome all kinds of disparity
by joint efforts. The inherent demographics processes in European countries do
not obey the unification plan, however. The OECD database
allows taking a specific look at the past and future of all countries … and
found that some countries go wrong way after joining the EU. Figure 1
demonstrates that three Baltic countries have been and extremely steep
depopulation trajectory after 1990. In 2015, they were by 15% to 25% smaller in
terms of total population when in 1990 (notice that they grew by 30% from 1950
to 1990). According to the OECD projection, three Baltic countries will lose from
35% to 40% of their population relative to 1990. This is rather grim future.

On the other hand, France and UK were, are and
continue to be on a healthy growth path with a perspective of 35% larger
population in 2015 than it was in 1990. Russia
has stabilized its population around 146 million, i.e. 99% of that in 1990, and
will not change much in the future.

The case of Germany is most illustrative for
the current political discussion of immigration in Europe. Germany loses now and
will be losing its population in the future. The OEDC projection says that the
UK will overtake Germany in 2045 and France in 2050. Germany is losing its biggest
population position against major European economies. This might be the reason
for mercantile Merkel to invite as many immigrants as possible to boost German population
and return it on the growing trajectory. Die Kanzlerin is wise.All in all, European Union will suffer strong demographic problems, which are related to emergent recognition of fading national identity.

Figure 1. Total population in selected European countries according to the OECD historical time series and population projections. All curves are normalized to their respective values in 1990.

5/22/16

We have discussed the incompatibility of real GDP data caused by the change in definition of the GDP deflator, dGDP, many times (in the USA - in 1977) [here, here, and here]. Time just strengthen our assumption that the growth of real GDP per capita (rGDPpc) in the USA is a linear function of time. The estimates of rGDPpc borrowed from the Total Economy Database illustrate this finding for all developed countries.
Here, we update (with two new annual estimates) the GDP curves, the original one and that corrected for the difference between the dGDP definition before and after 1977. Figure 1 shows details of the deviation between the dGDP and the consumer price index, CPI, as expressed by the cumulative inflation rates. Before 1977, the CPI (red) and dGDP (black dotted) lines are absolutely synchronized. Essentially, there is no difference in the GDP price deflator and the CPI. However, since 1978 one can observe that the CPI inflation rate is approximately equal to the rate of the GDP deflator change multiplied by a factor of 1.22, as shown in Figure 1. The coincidence between the observed CPI and the corrected dGDP (open circles) curves after 1977 is striking with Rsq>0.98.
The reason behind the change is not clear but the problem emerged with the difference between definitions used before and after 1977. (The Bureau of Economic Analysis warns economists that the real GDP time series is incompatible over time.) It is like to use the same nominal speed limit, say 45, after transition from miles to km per hour. By definition, real GDP is nominal GDP reduced by inflation rate. We are sure that it is necessary to use the same definition over time in order to have a real GDP time series without structural breaks. This is not the case in the data reported by the Bureau of Economic Analysis. Fortunately, the factor of 1.22 allows recovering the dGDP time series back in time using the strong statistical link between CPI and dGDP (1.22dGDP = CPI). The dashed line is the estimate of dGPD before 1977 when the same definition is applied as after 1977. We prefer to correct the dGDP time series instead of using the CPI for the period after 1977.
Figure 2 shows real GDP and real GDP per capita in the USA from 1929 to 2013. The latter time series has rather a linear trend since 1929 with Rsq. =0.97. The real GDP series deviates from the long term exponential trend since 2000 – the year then the rate of population growth fell below 1% per year.
In Figure 3, we correct real GDP per capita for the difference between CPI and dGDP after 1977 and compare the original and corrected time series. One can see that the corrected curve has Rsq.=0.98 and does not deviate from the long-term trend. Currently, the corrected growth rate goes exactly the linear long-term trend and strongly deviates from exponential function also shown in Figure 3.

USA will follow linear growth trend, which is identical to the rate of growth falling inversely proportionally to the level of real GDP per capita. Also, one should not use any data published by the BEA withour corrections.

Figure 1. Cumulative rates of CPI and dGDP inflation, original and scaled by a factor of 1.22.

Figure 2. Real GDP and real GDP per capita in the USA from 1929 to 2015. The latter time series has rather a linear trend since 1929. The real GDP series deviates from exponential trend since 2000 – the year then the rate of population growth fell below 1% per year.

Figure 3. The real GDP per capita time series corrected for the difference between CPI and dGDP since 1978. Linear trend is obvious in the corrected time series. Currently, the growth rate is slightly below the long-term trend.

5/21/16

In
this blog, we made a mid-term prediction on the evolution of crude oil price in
September
2012:

“The level of oil price in 2016 is expected between
$30 and $60 per barrel. “

This
is a revision to our oil price prediction as based on the difference between
the overall PPI and the index of crude oil. Figure 1 compares our previous prediction
in 2012 (upper panel) with actual oil price trajectory between 2012 and 2016
(lower panel). Both panels present price range, which expresses the slow fall
through 2016, with the uncertainty bounds for the long-term trend in oil price.
In April 2016, the observed oil price was close to the average value of $45 per
barrel in 2016. Following our analysis of the difference between the
core and headline CPI, we expect the price of oil to fall to $25 at a five
to ten year horizon.

The price range will likely hovert between $20 and
$30 since 2020.

Figure 1. The evolution of oil price since 2001 as estimated
from the differnce of the overall PPI and the PPI of crude petroleum.

5/19/16

In December 2014, we
posted on the falling producer price of steel and iron in 2014 and on
further fall in 2015-2016. This prediction was right and the PPI of iron and
steel has been falling from 226 (January 2014) to 173 (February 2016). The
overall PPI has also dropped by 20 points since 2014.Here we report that we foresee no general
change in the declining trend in the short-term. In 2017, we expect that the producer
price index of iron and steel will reach its bottom and start to grow, likely
during the next decade. Moreover, the overall PPI will stop falling and
dragging consumer prices down.

For price prediction of various commodities, our
general approach is based on the presence of long-term sustainable (linear and
nonlinear) trends in the evolution of the CPI and PPI in the United States [1, 2].
The difference between components of these indices is not a random one but is
rather a predetermined process. Using these trends, one can predict consumer
and producer price indices for select goods, services and commodities.

On Seeking Alpha,
we first reported on the evolution of the producer price index (PPI) for iron
and steel in July 2009.
We compared our earlier prediction from 2008 with the actual evolution of the
difference between the PPI of steel and iron and the headline PPI and made the
following forecast:

“In the short run, one
can expect a fast recovery of iron and steel prices to the level observed in
January-March 2008, i.e. the index will reach the level 210 to 220. However,
this recovery will not stretch into 2011, and the index of iron and steel will
be declining in the long run to the level of 2001, as depicted in Figure 3. In
other words, the period between 2008 and 2010 is characterized by very high
volatility, which will fade away after 2011.”

Figure 1 in this post reproduces Figure 3 from the
2009 post, where the green line gives a prediction of the future evolution. Since
2009, we made several updates considering new data on both PPIs (June 2010, February 2012, December 2012, August 2013, and aforementioned December 2014). According
to our long-term tradition, we revisit the previously predicted fall in the
producer price index of steel and iron and formulate a preliminary hypothesis
on the evolution in 2016-2017. Please notice that the green line was predicted in 2008.

Figure 2 displays the difference
between the PPI and the index for iron and steel (BLS code 101) since 1985. Between
1985 and 2000, the curve fluctuates around the zero line, i.e. there was no
linear trend in the absolute difference. The difference is characterized by a
sharp decline between 2001 and 2008. Our main assumption described in this post
was right - the negative trend observed before 2008, after a short period of
large fluctuations, started its transformation into a positive trend after 2010.
In Figure 2, the (slightly updated according to actual data between 2009 and
2011) new trend is shown by green line. This trend suggests that the PPI grows
faster (or falls slower) than the index of steel and iron by approximately 2
units of index per year.

Figure 3 demonstrates the most
recent period and confirms that our prediction for 2014 was correct – the
difference fluctuates around the green line. The overall trend is still positive, i.e. the
price of iron and steel falls faster than the overall PPI. There is no much room
left for further growth in the difference from the historical point of view,
however. One could suggest that the difference will reach its maximum somewhere
in 2017-2018 and then will turn to a negative trend similar to that observed between
2000 and 2008.

It might be good time to think about investment
in steel and iron. The pivot point is close.

Figure 1.
The prediction of steel and iron price made in 2009.

Figure 2. The difference of the PPI and the index
of steel and iron for the period between January 1985 and November 2014. The
green line was first introduced in 2008.

Figure 3. Same as in Figure 2 for the period
between January 2005 and April 2016. Green line predicts the evolution of the difference
after 2009. Upper panel from the 2014 post and the lower panel used the most
recent data. The overall trend is still positive, i.e. the price of iron and
steel falls faster than the overall PPI.

5/18/16

Since 2008, we have
been reporting that the evolution of various components of CPI and PPI in the
United States is not a random process but rather a predetermined one with
long-term sustainable trends [1, 2].
Using these trends, one can predict consumer and producer price indices for
various goods, services, and commodities. For example, in [3, 4], we
presented the evolution for many goods and services with varying weights in the CPI. There are more goods, services, and commodities
of interest for producers, consumers, and investors, however. Here we revisitand report the success of our
predictions for the index for copper
ores (the previous revision was
two years ago). This is an
example showing that some commodity prices are well predictable.

Figure 1
displays the difference between PPI and the index for copper ores since 1988.
This difference has a remarkable history: no big change between 1988 and 2003,
and then a sudden surge in the copper index started. The peak was reached in
the middle of 2006. It survived before the second quarter of 2008. Then the copper
index dropped by almost 300 units back to the overall PPI level. In 2009, the PPI
of copper increased above 500. Since
2012, the price index of copper has been falling along a linear trend. One may consider
these changes as associated with the rise-fall cycles in oil price, but there
is no one-to-one correspondence.

We have to
admit that there are no sustainable trends in the copper index and the future
of the copper ores index cannot be predicted at a ten-year horizon. Since 2012,
the difference is on its way to the zero level. Soon it may reach the trough observed
in 2009 (see Figure 2 for relative or normalized prices). Two years ago, we formulated
our prediction in a form that there was “no sign that the PPI of copper is
going to change its long-term decline”. And this was
a correct forecast – the copper price still follows negative trend. If the pivot
point for the current trend in the difference between copper PPI and the
overall PPI is around 0 then the copper price will be falling another two
years. This is in line with our prediction of further decrease in energy prices
in our
previous post.

Figure
1. Evolution of the price index of copper ores relative to the PPI.

Figure
2. Evolution of the difference between the overall PPI and the price index of
copper ores normalized to the PPI.

Aluminium price had a short-term excursions
into higher figures in 2014 and quickly returned to ite negative trend in 2015. This was a
dramatic fluctuation, but not the biggest one in the past 10 years as Figure 3
shows. In 2014, weexpected
the difference to follow the green line into 2016, and this fluctuation was
a major deviation from the expected behavior. Aluminum is a commodity, which
suddenly changed its behavior. Currently, the price of aluminum follows the
green-line-negative linear trend. From historical perspective, however, there
is no room for further fall in aluminum price as Figure 4 shiows. It might be the
best time to consider investment strategy for the next 5 years.

Figure
3. Evolution of the difference between the overall PPI and the price index of aluminum
scrap.

Figure 4. Historical time series
for the difference between aliminium and overall PPI. Aluminum price may soon
reache the historical minimum.

5/16/16

last time I
posted on the difference between the headline and core CPI in 2013. There is a good
reason to touch upon slow economic processes regularly but not often. Our concept of cyclic
evolution was formulated in 2007 and its updated version was posted many
times in this blog in the form of normalized difference (cCPI-CPI)/CPI.
Essentially, the concept says that the future trajectory has to repeat the path
observed thirty years ago, i.e. the cycle has a 30 years period. Figure 1
presents the state of the difference as observed at the end of 2013 (dotted red
line) and the predicted trajectory (blue dotted line), which is the current
curve shifted by 30 years ahead. Figure 2 presents the current state and proves
our hypothesis.

We
have been routinely reporting on the difference between the headline and core
CPI since
2008 and predicted the era of low energy+food prices (core CPI is the
headline CPI less food and energy) since 2014 and for the following 20 years.
So far, it is a good prediction fitting our concept since 2007. For energy
companies, Figure 2 implies that oil/energy prices will be on a negative trend
until 2030. We are just in the middle of the fall, which is supposed to be
dramatic in the next two to four years. Fasten your belts.

Figure 1. The difference between core
(cCPI) and headline (CPI) CPI normalized to CPI. The period of cycle is 30
years and dotted blue line presents the
future of the normalized difference before 2044.

Figure 2. The current state of our
prediction. Red line follows up the blue line.

5/13/16

This post extends our previous analysis of the long term GDP growth in developed countries with BRIC.

Table 1 lists average annual increment of GDP per capita
(1990 USD) in developed countries. The best countries demonstrated increments
above $350 per year. Many European countries are between $300 and $350 per
year. It is possible to conclude that intertial economic growth is somewhere
between $320 and $390 per year (in
PPP 1990 US dollars). Some European countries demonstrate poor performance,
e.g. Italy, France, Portugal, Greece.

The long term annual increment for a given country completely
defines the rate of economic growth. According to their GDP per capita levels,
all developed countries are characterized by the rate of inertial growth within
the range between 1.5% per year and 2.5% per year. One should not expect higher
rates, with a low probability of short term fluctuations.

Let’s apply the notation of inertial economic growth to BRIC
countries and assess their performance in terms of their potential rate of inertial
growth. Table 2 lists mean annual increment in GDP per capita in four BRIC
countries, which are rather low: from $64
in India to $114 in China over the same period between 1960 and 2015. This is
three to four times smaller than that in developed countires. Hence, BRIC
countries demonstrate poor performance over longer period.

However, China figures look much better at a shorter interval
of the past 20 years. The annul increment is $242, i.e. approximately 70% of
that in Austria and at the level of France. Since the level of GDP per capita
in China is extremely small by European standards, the rate of growth is much higher – between 3% and 10% per year. (Here we
use data for China, not China-old,
from the Total Economy Database.)

The current rate of 3% to 4% per year is much smaller than one could expect when China would grow along
intertial trajectory of European countries. Figures 1 thru 4 depict various
versions of growth trajectories for BRIC countries.

Table 1. Mean annual increment of GDP per capita

Mean, 1960-2015

Austria

340

Belgium

326

Denmark

282

Finland

320

France

274

Germany

281

Greece

159

Ireland

357

Italy

212

Netherlands

304

Norway

363

Portugal

191

Spain

240

Sweden

313

Switzerland

262

United Kingdom

286

Canada

323

United States

387

Australia

338

New Zealand

203

Japan

345

Table 2. Annual GDP per
capita increment in BRIC countries

1960-2015

1995-2015

China

114

242

India

64

134

Brazil

83

82

Russia

105

201

Fig. 1. The evolution of real GDP per capita in China
from 1960 to 2015. Three graphs demonstrate annual increment as a function of
GDP per capita, annual increment as a function of time, and the growth rate
(1/year) as a function of GDP per capita.

Fig. 2. The evolution of real GDP per capita in India
from 1960 to 2015. Three graphs demonstrate annual increment as a function of
GDP per capita, annual increment as a function of time, and the growth rate
(1/year) as a function of GDP per capita.

Fig. 3. The evolution of real GDP per capita in Brazil
from 1960 to 2015. Three graphs demonstrate annual increment as a function of
GDP per capita, annual increment as a function of time, and the growth rate
(1/year) as a function of GDP per capita.

Fig. 4. The evolution of real GDP per capita in Russia
from 1960 to 2015. Three graphs demonstrate annual increment as a function of
GDP per capita, annual increment as a function of time, and the growth rate
(1/year) as a function of GDP per capita.

Check these journals

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